Video compression has become a source of different research studies. It is necessary in order to address channel bandwidth limitations and growing video demand, including digital libraries and streaming media delivery via the Internet. A video is a number of frames captured by a camera while a scene is a series of consecutive frames captured from a specific narrative viewpoint. To compress a video, firstly, the intra frames are separated from inter frames using scene change detection methods. Then, block-based motion estimation algorithms are used to eliminate the temporal redundancy between successive frames. This paper describes some scene change detection methods for use on the uncompressed video to detect scene types such as cut, dissolve, wipe, etc. Absolute Frame Difference (AFD), Mean Absolute Frame Differences (MAFD), Mean Histogram Absolute Frame Difference (MHAFD), and Maximum Gradient Value (MGV) techniques are adaptively tested on different video types to identify accurate scene change in both low and high object motion scenes. Test results show that the proposed approach (MHAFD ) obtains a better accuracy, F1-SCOR measure of 100%, especially for cuts and gradual transitions (wipe) video types. Dissolve scene change is detected with a high precision of 100% (i. e., no false detection) with the (MAFD) detector. Besides, in terms of time complexity for analyzing all the video samples, the proposed method (MHAFD) provides the best result compared to the selected detectors.